Senior Applied Scientist, Generative AI Innovation Center

Senior Applied Scientist, Generative AI Innovation Center

Job ID: Amazon Web Services Singapore Private Limited

Overview

Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. Starting in 2024, the Innovation Center launched a new Custom Model and Optimization program to help customers develop and scale highly customized generative AI solutions. The team helps customers imagine and scope bespoke use cases that will create the greatest value for their businesses, define paths to navigate technical or business challenges, develop and optimize models to power their solutions, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We're looking for Applied Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems.

Responsibilities

• Collaborate with AI/ML scientists and architects to research, design, develop, and evaluate generative AI solutions to address real-world challenges
• Interact with customers directly to understand their business problems, aid them in implementation of generative AI solutions, brief customers and guide them on adoption patterns and paths to production
• Help customers optimize their solutions through approaches such as model selection, training or tuning, right-sizing, distillation, and hardware optimization
• Provide customer and market feedback to product and engineering teams to help define product direction

BASIC QUALIFICATIONS

• PhD degree in computer science, engineering, mathematics, operations research, or in a highly quantitative field plus 5 years of relevant experience, or Master's degree plus 10 years of relevant work experience
• 5+ years of hands on experience with Python to build, train, and evaluate models
• 5+ years of experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high- performance computing
• 2+ years demonstrated experience with Large Language Model (LLM) and Foundational Model post-training, continual pre-training, fine-tuning, or reinforcement learning techniques.
• Scientific publication track record at top-tier AI/ML/NLP conferences or journals

PREFERRED QUALIFICATIONS

• Demonstrated experience with building LLM-powered agentic workflow, orchestration, and agent customization
• Experience with model optimization techniques (quantization, distillation, compression, inference optimization etc.)
• Experience with open-source frameworks for model customization like trl, verl, and for building LLM-powered applications like LangChain, LlamaIndex, and/ or similar tools
• Strong communication skills, with attention to detail and ability to convey rigorous technical concepts and considerations to non-experts
• Demonstrated ability to identify and frame technical problems from broad product-level and business-level problem areas.
• Track record of leading the design, implementation and delivery of scientifically- complex solutions that span multiple teams.
• Experience driving scientific agenda and technical strategy in a team, including building consensus on technical approaches and mentoring other scientists to improve their technical capabilities.

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